2023 Silicon Valley Cybersecurity Conference (SVCC)最新文献

Guard Cache: Creating False Cache Hits and Misses To Mitigate Side-Channel Attacks 保护缓存:创建错误的缓存命中和错过，以减轻侧通道攻击

2023 Silicon Valley Cybersecurity Conference (SVCC) Pub Date : 2023-05-17 DOI: 10.1109/SVCC56964.2023.10165527

Fernando Mosquera, K. Kavi, Gayatri Mehta, L. John

{"title":"Guard Cache: Creating False Cache Hits and Misses To Mitigate Side-Channel Attacks","authors":"Fernando Mosquera, K. Kavi, Gayatri Mehta, L. John","doi":"10.1109/SVCC56964.2023.10165527","DOIUrl":"https://doi.org/10.1109/SVCC56964.2023.10165527","url":null,"abstract":"Cache side-channel attacks have exposed serious security vulnerabilities in modern architectures. These attacks rely on measuring cache access times to determine if an access to an address is a hit or a miss in the cache. Such information can be used to identify which addresses were accessed by the victim, which in turn can be used to reveal or at least guess the information accessed by the victim. Mitigating the attacks while preserving the performance has been a challenge. The hardware mitigation techniques used in the literature include complex cache indexing mechanisms, partitioning cache memories, and hiding or undoing the effects of speculation. In this paper, we present a Guard Cache to obfuscate cache timing, making it more difficult for cache timing attacks to succeed. We create false cache hits by using the Guard Cache as a Victim Cache, and false cache misses by randomly evicting cache lines. Our obfuscations can be turned-on and turned-off on demand to protect critical sections or randomly to further obfuscate cache access times. We show that our false hits cause very minimal performance penalties ranging between −0.2% to 3.0% performance loss, while false misses can cause higher performance losses. We also show that our approach causes different number of cache hits and misses and different addresses causing misses when compared to traditional caches, demonstrating that common side-channel attacks such as Prime & Probe, Flush & Reload or Evict & Time are likely to misinterpret victims’ memory accesses. We use very small Guard Caches (1KiB-2KiB at L1 or 2KiB-4KiB at L2) requiring very minimal additional hardware. The hardware needed for random evictions is also minimal.","PeriodicalId":243155,"journal":{"name":"2023 Silicon Valley Cybersecurity Conference (SVCC)","volume":"70 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129139779","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Autonomous Lending Organization on Ethereum with Credit Scoring 具有信用评分的以太坊自治借贷组织

2023 Silicon Valley Cybersecurity Conference (SVCC) Pub Date : 2023-05-17 DOI: 10.1109/SVCC56964.2023.10164922

Thomas H. Austin, Katerina Potika, C. Pollett

引用次数: 0

OFMCDM/IRF: A Phishing Website Detection Model based on Optimized Fuzzy Multi-Criteria Decision-Making and Improved Random Forest OFMCDM/IRF:基于优化模糊多准则决策和改进随机森林的钓鱼网站检测模型

2023 Silicon Valley Cybersecurity Conference (SVCC) Pub Date : 2023-05-17 DOI: 10.1109/SVCC56964.2023.10165344

Md. Abdullah Al Ahasan, Mengjun Hu, Nashid Shahriar

引用次数: 0

Trustworthy of Implantable Medical Devices using ECG Biometric 使用心电生物识别技术的植入式医疗器械的可靠性

2023 Silicon Valley Cybersecurity Conference (SVCC) Pub Date : 2023-05-17 DOI: 10.1109/SVCC56964.2023.10164853

Nima Karimian, Sara Tehranipoor, Thomas Lyp

引用次数: 0

Malware Detection through Contextualized Vector Embeddings 基于情境化向量嵌入的恶意软件检测

2023 Silicon Valley Cybersecurity Conference (SVCC) Pub Date : 2023-05-17 DOI: 10.1109/SVCC56964.2023.10165170

Vinay Pandya, Fabio Di Troia

{"title":"Malware Detection through Contextualized Vector Embeddings","authors":"Vinay Pandya, Fabio Di Troia","doi":"10.1109/SVCC56964.2023.10165170","DOIUrl":"https://doi.org/10.1109/SVCC56964.2023.10165170","url":null,"abstract":"Detecting malware is an integral part of system security. In recent years, machine learning models have been applied with success to overcome this challenging problem. The aim of this research is to apply context-dependent word embeddings to classify malware. We extract opcodes from the malware samples and use them to generate the embeddings that train the classifiers. Transformers are a novel architecture that utilizes self-attention to handle long-range dependencies. Different transformer architectures, namely, BERT, DistilBERT, AIBERT, and RoBERTa, are implemented in this work to generate context-dependent word embeddings. Apart from using transformer models, we also experimented with ELMo, a bidirectional language model which can generate contextualized opcode embeddings. These embeddings are used to train our machine learning models in classifying samples from different malware families. We compared our contextualized results with context-free embeddings generated by Word2Vec, and HMM2Vec algorithms. The classification algorithms trained on our embeddings consist of Resnet-18 CNN, Random Forest, Support Vector Machines (SVMs), and k-Nearest Neighbours (k-NNs).","PeriodicalId":243155,"journal":{"name":"2023 Silicon Valley Cybersecurity Conference (SVCC)","volume":"312 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121302242","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

BlockNIC: SmartNIC assisted Blockchain BlockNIC: SmartNIC辅助区块链

2023 Silicon Valley Cybersecurity Conference (SVCC) Pub Date : 2023-05-17 DOI: 10.1109/SVCC56964.2023.10165427

Eish Kapoor, Gavin Jampani, Sean Choi

引用次数: 0

Lightweight and Effective Website Fingerprinting Over Encrypted DNS 轻量级和有效的网站指纹加密DNS

2023 Silicon Valley Cybersecurity Conference (SVCC) Pub Date : 2023-05-17 DOI: 10.1109/SVCC56964.2023.10165086

Yong Shao, K. Hernandez, Kia-Min Yang, Eric Chan-Tin, M. Abuhamad

引用次数: 0

A Curriculum Framework for Autonomous Network Defense using Multi-agent Reinforcement Learning 基于多智能体强化学习的自主网络防御课程框架

2023 Silicon Valley Cybersecurity Conference (SVCC) Pub Date : 2023-05-17 DOI: 10.1109/SVCC56964.2023.10165310

Roberto G. Campbell, M. Eirinaki, Younghee Park

{"title":"A Curriculum Framework for Autonomous Network Defense using Multi-agent Reinforcement Learning","authors":"Roberto G. Campbell, M. Eirinaki, Younghee Park","doi":"10.1109/SVCC56964.2023.10165310","DOIUrl":"https://doi.org/10.1109/SVCC56964.2023.10165310","url":null,"abstract":"Early threat detection is an increasing part of the cybersecurity landscape given the growing scale and scope of cyberattacks in the recent years. Increasing exploitation of software vulnerabilities, especially in the manufacturing sector, demonstrates the ongoing need for autonomous network defense. In this work, we model the problem as a zero-sum Markov game between an attacker and defender reinforcement learning agents. Previous methods test their approach on a single topology or limit the agents to a subset of the network. However, real world networks are rarely fixed and often add or remove hosts based on demand, link failures, outages, or other factors. We consider two types of topologies: static topologies that remain fixed throughout training and a dynamic topology curriculum. The proposed robust training curriculum incorporates network topologies to build more general, capable agents. We also use Proximal Policy optimization (PPO) which offers a good balance of computational complexity and convergence speed. We evaluate various threat scenarios in terms of the exploitability and impact and conclude that the curriculum improves the defender’s win rate over training on a static topology by exposing the agent to more challenging environments over time.","PeriodicalId":243155,"journal":{"name":"2023 Silicon Valley Cybersecurity Conference (SVCC)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129184109","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Multivariate Time Series Anomaly Detection with Deep Learning Models Leveraging Inter-Variable Relationships 利用变量间关系的深度学习模型的多变量时间序列异常检测

2023 Silicon Valley Cybersecurity Conference (SVCC) Pub Date : 2023-05-17 DOI: 10.1109/SVCC56964.2023.10165468

Changmin Seong, Dongjun Lim, Jiho Jang, Jonghoon Lee, Jong-Geun Park, Yun-Gyung Cheong

{"title":"Multivariate Time Series Anomaly Detection with Deep Learning Models Leveraging Inter-Variable Relationships","authors":"Changmin Seong, Dongjun Lim, Jiho Jang, Jonghoon Lee, Jong-Geun Park, Yun-Gyung Cheong","doi":"10.1109/SVCC56964.2023.10165468","DOIUrl":"https://doi.org/10.1109/SVCC56964.2023.10165468","url":null,"abstract":"This paper presents a system for multivariate time series anomaly detection using deep learning, with an added module to reflect variable relationships. The system uses an autoencoder to extract latent variables that reflect the time series characteristics of the variables, and calculates variable importance using the similarities among the variables. To evaluate the proposed method, experiments were conducted using three similarity measures: cosine similarity, distance correlation, and DTW. Four time series datasets were used for evaluation, and the results showed that the proposed model outperformed the baseline model in HAI 22.04 and HAI 21.03 datasets. For the WADI dataset, the Fl-score improved only when using cosine similarity, while the TaPR-Fl score improved only when using DTW. However, no performance improvement was observed in the SWaT dataset. These results suggest that the effectiveness of utilizing inter-variable relationships is dependent on the characteristics of the data and the similarity calculation method employed. Therefore, a careful selection of the appropriate similarity calculation method for a given dataset is necessary to achieve optimal performance improvements.","PeriodicalId":243155,"journal":{"name":"2023 Silicon Valley Cybersecurity Conference (SVCC)","volume":"72 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129723092","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Privacy-Preserving Trust Management For Vehicular Communications and Federated Learning 车辆通信和联邦学习的隐私保护信任管理

2023 Silicon Valley Cybersecurity Conference (SVCC) Pub Date : 2023-05-17 DOI: 10.1109/SVCC56964.2023.10165137

S. Byun, Arijet Sarker, Ken Lew, J. Kalita, Sang-Yoon Chang

{"title":"Privacy-Preserving Trust Management For Vehicular Communications and Federated Learning","authors":"S. Byun, Arijet Sarker, Ken Lew, J. Kalita, Sang-Yoon Chang","doi":"10.1109/SVCC56964.2023.10165137","DOIUrl":"https://doi.org/10.1109/SVCC56964.2023.10165137","url":null,"abstract":"Cellular networking is evolving as a wireless technology to support a wide range of applications in vehicular communication. Cellular technologies enable vehicles to communicate with many applications to improve driving experience. All of these applications are not under the administration of the same authority. Secure Credential Management System (SCMS) is a Public Key Infrastructure (PKI) which provides certificates to vehicles to preserve vehicular privacy and supports many vehicular applications such as Basic Safety Messages (BSMs), misbehavior reporting, etc. On the other hand, privacy-preserving applications like federated learning (used to enable data-driven machine learning for better self-driving experience while protecting the vehicular data privacy) are outside of this SCMS-managed vehicular network and have separate PKI structure managed by a different authority. Since the trust management and PKI for vehicular vs. federated learning have been studied and developed separately, there is a need to establish trust between these two PKIs for secure communication between vehicles and Federated Learning Servers (FLSs). In this work, we demonstrate and analyze how these two orthogonal PKIs can establish trust with each other and thus, the end entities (vehicles and FLSs) can securely communicate with each other in an efficient manner to maintain vehicular privacy utilizing SCMS.","PeriodicalId":243155,"journal":{"name":"2023 Silicon Valley Cybersecurity Conference (SVCC)","volume":"173 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124218610","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0